I am a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC). My research is at the interface of mechanism and market design, optimization and operations research (specifically integer programming), and machine learning.
I received a PhD in the Computer Science Department at Carnegie Mellon University, where I was advised by Nina Balcan and Tuomas Sandholm. During the PhD, I spent a summer working on recommender systems at Google Research. Before the PhD, I received a B.S. in math and computer science from Caltech.
My PhD Thesis: Mechanism Design and Integer Programming in the Data Age
Research Papers
- Revenue-Optimal Efficient Mechanism Design with General Type Spaces
with Nina Balcan and Tuomas Sandholm - Weakest Bidder Types and New Core-Selecting Combinatorial Auctions
with Nina Balcan and Tuomas Sandholm - New Sequence-Independent Lifting Techniques for Cover Inequalities and When They Induce Facets
with Ellen Vitercik, Nina Balcan, and Tuomas Sandholm
International Joint Conference on Artificial Intelligence (IJCAI), 2025
Best poster award (honorable mention) at MIP workshop, 2024 - Increasing Revenue in Efficient Combinatorial Auctions by Learning to Generate Artificial Competition
with Nina Balcan and Tuomas Sandholm
AAAI Conference on Artificial Intelligence (AAAI), 2025 - Bicriteria Multidimensional Mechanism Design with Side Information
with Nina Balcan and Tuomas Sandholm
Conference on Neural Information Processing Systems (NeurIPS), 2023
[sigecom reading list] - Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems
with Martin Mladenov and Craig Boutilier
RecSys Workshop on Causality, Counterfactuals, and Sequential Decision Making (CONSEQUENCES), 2023
[recsys talk] - Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts
with Nina Balcan, Tuomas Sandholm, and Ellen Vitercik
Conference on Neural Information Processing Systems (NeurIPS), 2022
Oral presentation (top 2% of submissions)
[video] - Maximizing Revenue under Market Shrinkage and Market Uncertainty
with Nina Balcan and Tuomas Sandholm
Conference on Neural Information Processing Systems (NeurIPS), 2022
[video] - Improved Sample Complexity Bounds for Branch-and-Cut
with Nina Balcan, Tuomas Sandholm, and Ellen Vitercik
International Conference on Principles and Practice of Constraint Programming (CP), 2022 - Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond
with Nina Balcan, Tuomas Sandholm, and Ellen Vitercik
Conference on Neural Information Processing Systems (NeurIPS), 2021
Spotlight presentation (top 3% of submissions)
[video] - Learning Within an Instance for Designing High-Revenue Combinatorial Auctions
with Nina Balcan and Tuomas Sandholm
International Joint Conference on Artificial Intelligence (IJCAI), 2021
[proceedings version] [video] - Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs
with Nina Balcan and Tuomas Sandholm
International Joint Conference on Artificial Intelligence (IJCAI), 2020
[video] - Incentive Compatible Active Learning
with Federico Echenique
Innovations in Theoretical Computer Science Conference (ITCS), 2020
[itcs talk] - Learning Time Dependent Choice
with Zachary Chase
Innovations in Theoretical Computer Science Conference (ITCS), 2019 - Walks on Primes in Imaginary Quadratic Fields